Why Financial Services must cash in on the analytics wave

Financial
services are under constant pressure to find innovative ways to grow their
revenue and assets. Simultaneously, competition is extreme as financial service
providers compete for the wallet share of customers. To beat the competition
and drive sales, financial services are looking at versatile technology
channels like the cloud, advanced analytics, and artificial intelligence to
acquire new customers and building more productive and profitable customer
relations.

The
leading firms in the game including offshoots of HSBC, Citi, Barclays, and
others have already started harnessing their ever-growing volumes of data to
generate deeper insights, enabling them to acquire and expand their customer
base. These companies aremaking sense of the data at hand byleveraging
analytics technologies like Deep Learning, Machine Learning, and Artificial Intelligence.

The
financial firms data driven analytics need data that they can trust transparently, which help them
verify data quality, data provenance, and traceability. They then seem to
deliver an improved customer experience and business benefits likerisk-optimization,
improvedpayments and collection, and enhanced productivity.

Reasons
to use data-driven analytics for financial services

1.
Improve the customer experience

Customer
experience is now the centerpiece of every service. By implementing data-driven
analytics, financial service companies aim to be able to provide a better
customer experience in several ways:

·
Customized financial service

The
companies can gather and use data based on customer satisfaction parameters,
buying history, demographic data, preferences, and buyingbehavior. The data can
help them provide tailor-made products and services and “personalized” offers.
Driven by this data, the firms can recommend the most suitable products for
their customers to buy. This ultimately contributes to a more custom customer
experience, customer satisfaction, and customer retention. For example,
American Express uses Machine Learning for a wide range of interactions to
personalize customer offers and streamline sales.

·
Automated-advisor services

Automated-advisors,
or robo-advisors, were launched in 2008 in the midstof the financial crisis.
They are designed to provide financial advice or investment management with
minimal human intervention. Companies can implement such automated services to
offer digital financial advice driven by mathematical rules and algorithms. Specially
designed software executes these algorithms to deliver investment advice just
like a human advisor. Such automation can also manage portfolios and provide
better investment suggestions by analyzing customer risk profiles. For
instance, TransUnion has launched a self-service platform which helps its
customers assess their profiles for risks and take better investment decisions.

·
Chatbots

Much
has been written about the use of chatbots already. Suffice it to say that
these have now moved beyond the hype and into everyday reality. The financial
services data driven sector can take advantage of Artificial Intelligence technology for
improved customer experience. AI-based chatbots can help customers to provide
details, authenticate, and conduct financial transactions quickly. For
customers, the information is available on their fingertips, eliminating the
need for human interference. On the other hand, the data captured in such
interactions can help banks understand customer’s behavior and their buying
decision, allowing them todeliver a more personalized user experience.

2.
Fraud protection For Financial Services

Modern
financial institutes faceterrifying fraud risks. As per the reports, financial
firms had lost more than $2.2 billion in 2016 to fraud and malpractice. To
prevent such damage, data-driven analytics can help firms to identify threats
through a deep understanding of customer data. Analytics can detect unusual
activity and raise red flags when something untoward seems to be taking place.

Of
course, financial firms collect versatile data including customer name, email,
IP address, phone number, payment method, time of payment, CVV, currency, to
name a few. They also build up massive amounts of data on transaction behavior,
by specific customers and across categories. This is a critically important treasure
trove of data in which the solution to combat fraud lies. Financial firms can
use data from the transaction to the aggregate-level to fight fraud.

3.
Automate business process

Data-driven
analytics promises the transformative power of automation for financial firms.
The aim is nothing lessthan total data integrity with zero manual errors.

They
can streamline financial and transaction process, ensuring smooth
operations.

The
data gathering, data transfer, and storage can be automatedand validated by
integrating different systems.

Such automation
can free up key executives to make strategic decisions which need human intervention, human experience, and intuitive
decision-making abilities. Of course, reporting and monitoring processes
can also gain from automation, giving executives the opportunity to get a
complete picture of their operations at all times.

4. Benefits
of predictive analytics in financial services

Creating
a robust data-drivenecosystem will help financial services firms predict
operational demands through predictive analytics. This predictive capability
can help drive great benefits. For eg.,

Firms
could improve operational efficiency by identifying and eliminating the roots
of problematic transactions.

Conclusion

As
financial service companies gain value from data-driven analytics, they seem
set to unleash innovation and create organizational enthusiasm for using data. These
companies could create new revenue opportunities by monetizing existing data
assets. Analytics could usher in a new financial services revolution driven by
innovation.